

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def       lowercase					(	__A :					Tuple ,    __A :					Tuple		)				->			List[Any]:





    '''simple docstring'''




    assert isinstance(__A ,    __A		)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory""" ,    [False, True]		)
def       lowercase					(	__A :					Union[str, Any] ,    __A :					int ,    __A :					Any ,    __A :					Any		)				->			Any:





    '''simple docstring'''




    snake_case							:		str        =		tmp_path / """cache"""
    snake_case							:		Union[str, Any]        =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case							:		Any        =		SqlDatasetReader(
            """dataset""" ,    """sqlite:///""" + sqlite_path ,    cache_dir=__A ,    keep_in_memory=__A		).read()
    _check_sql_dataset(__A ,    __A		)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features""" ,    [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,    )
def       lowercase					(	__A :					int ,    __A :					str ,    __A :					Optional[Any] ,    __A :					int		)				->			int:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		tmp_path / """cache"""
    snake_case							:		List[Any]        =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    snake_case							:		List[Any]        =		features.copy() if features else default_expected_features
    snake_case							:		Any        =		(
        Features({feature: Value(__A		) for feature, dtype in features.items()}		) if features is not None else None
    )
    snake_case							:		List[str]        =		SqlDatasetReader("""dataset""" ,    """sqlite:///""" + sqlite_path ,    features=__A ,    cache_dir=__A		).read()
    _check_sql_dataset(__A ,    __A		)







def       lowercase					(	__A :					Optional[Any]		)				->			Union[str, Any]:





    '''simple docstring'''




    with contextlib.closing(sqlitea.connect(__A		)		) as con:
        snake_case							:		int        =		con.cursor()
        cur.execute("""SELECT * FROM dataset"""		)
        for row in cur:
            yield row







@require_sqlalchemy
def       lowercase					(	__A :					Dict ,    __A :					Any ,    __A :					Dict		)				->			int:





    '''simple docstring'''




    snake_case							:		Tuple        =		tmp_path / """cache"""
    snake_case							:		List[Any]        =		os.path.join(__A ,    """tmp.sql"""		)
    snake_case							:		Optional[int]        =		SqlDatasetReader("""dataset""" ,    """sqlite:///""" + sqlite_path ,    cache_dir=__A		).read()
    SqlDatasetWriter(__A ,    """dataset""" ,    """sqlite:///""" + output_sqlite_path ,    num_proc=1		).write()

    snake_case							:		Any        =		iter_sql_file(__A		)
    snake_case							:		Tuple        =		iter_sql_file(__A		)

    for rowa, rowa in zip(__A ,    __A		):
        assert rowa == rowa







@require_sqlalchemy
def       lowercase					(	__A :					List[Any] ,    __A :					str ,    __A :					Optional[Any]		)				->			str:





    '''simple docstring'''




    snake_case							:		List[Any]        =		tmp_path / """cache"""
    snake_case							:		Tuple        =		os.path.join(__A ,    """tmp.sql"""		)
    snake_case							:		Tuple        =		SqlDatasetReader("""dataset""" ,    """sqlite:///""" + sqlite_path ,    cache_dir=__A		).read()
    SqlDatasetWriter(__A ,    """dataset""" ,    """sqlite:///""" + output_sqlite_path ,    num_proc=2		).write()

    snake_case							:		str        =		iter_sql_file(__A		)
    snake_case							:		Optional[int]        =		iter_sql_file(__A		)

    for rowa, rowa in zip(__A ,    __A		):
        assert rowa == rowa







@require_sqlalchemy
def       lowercase					(	__A :					Optional[int] ,    __A :					Dict ,    __A :					Dict		)				->			Tuple:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		tmp_path / """cache"""
    snake_case							:		Dict        =		os.path.join(__A ,    """tmp.sql"""		)
    snake_case							:		Optional[Any]        =		SqlDatasetReader("""dataset""" ,    """sqlite:///""" + sqlite_path ,    cache_dir=__A		).read()
    with pytest.raises(__A		):
        SqlDatasetWriter(__A ,    """dataset""" ,    """sqlite:///""" + output_sqlite_path ,    num_proc=0		).write()




"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
